We live in a world where each day tons and tons of data is generated from millions of sources. Companies and organizations thrive for this data in order to acquire valuable information that helps them understanding their customer needs and demands. This valuable insight collaborates in improving the services and products – thus enhancing the overall business and profits. Filtering out such significant information thus requires employing some data mining algorithms. Data mining is a wide area of study that is further developing day by day and is very useful in deriving important information and coherence from large and raw datasets. When we talk about one very well-known field of business, known as Market Basket Analysis – data mining has significantly affected this sector. As the name suggests, it is an analysis of shopper’s basket at a mart. We generally see various items arranged on the shelves in the malls and supermarkets; we also observe certain products recommended to us when we shop online. All of this is worked in the back-end with the help of data-mining algorithms that provide a proper analysis of customer buying patterns and hence it makes the relations and suggests them to the customers, which in turn results in an enhanced sales. Technically, association rule mining and frequent itemset mining is done for such analysis. These algorithms are also used in designing various games and in recommendation systems. In this paper we are thus understanding these algorithms and compare the efficiency of the most common ones on the basis of factors such as time, support and memory consumed.

Data mining, Association rules, Apriori, FP-Growth, Eclat, dEclat

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